UCLA

First principle methods and data-driven approach were employed to investigate the intri- cate mechanical and electrical behaviors and properties of various nanotubes, both real and reimagined. A real space, helical and cyclic symmetry adapted, Kohn Sham DFT code— HelicalDFT was utilized to explore the torsional, extensional and electronic properties of group-IV nanotubes, unveiling unique mechanical and electronic responses. The study fur- ther delves into Carbon Kagome Nanotubes (CKNTs) and novel P2C3 nanotubes, highlight- ing their potential in material science due to distinctive electronic characteristics. A data- driven approach using machine learning predicts the electronic structure of nanotubes even under deformation, enhancing the understanding of nanomaterial behavior. Additionally, the integration of ellipsoidal coordinate systems within the current computational frame- work to advance in future evaluation of Gaussian curvature effects, opening new avenues for the design of nanomaterials with customized properties. This comprehensive research provides valuable insights into nanotube properties, offering a robust framework for future material science explorations and technological applications.